Ethical considerations in the use of Machine Learning for research and statistics

Ethical considerations in the use of Machine Learning for research and statistics
UK Statistics Authority 26 October 2021

• This high-level guidance explores ethical considerations associated with the use of machine learning techniques for research and statistical purposes. This guidance is not exhaustive, but aims to assist and support analysts, researchers, data scientists, and statisticians navigating the ethical issues surrounding machine learning based projects. Links to further resources are provided if you would like to read about particular aspects in more detail.

Ethics and governance of artificial intelligence for health

Ethics and governance of artificial intelligence for health
WHO 28 June 2021

• The report identifies the ethical challenges and risks with the use of artificial intelligence of health, six consensus principles to ensure AI works to the public benefit of all countries. It also contains a set of recommendations that can ensure the governance of artificial intelligence for health maximizes the promise of the technology and holds all stakeholders – in the public and private sector – accountable and responsive to the healthcare workers who will rely on these technologies and the communities and individuals whose health will be affected by its use.

Switched on: How do we get the best out of automation and AI in health care?

Switched on: How do we get the best out of automation and AI in health care?
Health Foundation 23 June 2021

• This report draws on Health Foundation research along with online YouGov surveys of over 4,000 UK adults and over 1,000 NHS staff. The findings show that 45% of NHS staff surveyed said they felt that patients might not accept artificial intelligence or be suspicious of it and 39% felt that staff shortages or inadequate equipment might make it difficult to use these technologies properly. 
• The Health Foundation calls on Government to explicitly address the workforce, skills and infrastructure needs of the NHS in order to exploit new and established technologies successfully over the long term.

Ensuring patient and public involvement in the transition to AI-assisted mental health care: A systematic scoping review and agenda for design justice

Ensuring patient and public involvement in the transition to AI-assisted mental health care: a systematic scoping review and agenda for design justice.
Health Expectations, 24(4), 2021, pp.1072-1124.

• A systematic review of the research identified three main themes which reflect the challenges and opportunities associated with PPI in AI-assisted mental health care: (a) applications of AI technologies in mental health care; (b) ethics of public engagement in AI-assisted care; and (c) public engagement in the planning, development, implementation, evaluation and diffusion of AI technologies.

Regulating the AI ecosystem - Multi-agency advisory service

Regulating the AI ecosystem - Multi-agency advisory service
NHSX

• The NHSX AI Lab will fund programmes to enable a world leading, safe and ethically robust ecosystem for the development and deployment of AI technologies. 
• One of the first programmes is a Multi-agency advisory service which aims to give innovators and health and care providers developing AI technologies a one stop shop for support, information and guidance on regulation and evaluation. Bodies participating in the project are the National Institute of Health Excellence (NICE), Care Quality Commission (CQC), Medicines and Healthcare Products Regulatory Agency (MHRA) and the Health Research Authority (HRA). 
• Other programmes support streamlining the process for technological review, work to scale the development of synthetic datasets, and post market surveillance.

Health information technology and digital innovation for national learning health and care systems

Health information technology and digital innovation for national learning health and care systems
The Lancet Digital Health June 2021 v3(6) p e383-e396 

• A discussion of the opportunities around the use of digital health technology to support policy and planning, public health, and personalisation of care. Innovations include integrating electronic health records across health and care providers, investing in health data science research, generating real-world data, developing artificial intelligence and robotics, and facilitating public–private partnerships. To address the ethical issues there is a need to develop regulatory frameworks for the development, management, and procurement of artificial intelligence and health information technology systems, create public–private partnerships, and ethically and safely apply artificial intelligence in the National Health Service.

Ethics, Transparency and Accountability Framework for Automated Decision-Making

Ethics, Transparency and Accountability Framework for Automated Decision-Making
Cabinet Office, Central Digital & Data Office and the Office for Artificial Intelligence 13 May 2021

• A 7 point framework which will help government departments with the safe, sustainable and ethical use of automated or algorithmic decision-making systems.

Putting Good into Practice: A public dialogue on making public benefit assessments when using health and care data

Putting Good into Practice: A public dialogue on making public benefit assessments when using health and care data
National Data Guardian 14 April 2021

• This report details the findings of a dialogue with more than 100 members of the public about how to make sure that health and care data is used in ways that benefit people and society.

Women’s attitudes to the use of AI image readers: a case study from a national breast screening programme

Women’s attitudes to the use of AI image readers: a case study from a national breast screening programme
BMJ Health & Care Informatics 2021;28:e100293. doi: 10.1136/bmjhci-2020-100293

• This study uses survey results and focus groups to examine the attitudes of women, both current and future users of breast screening, towards the use of AI in mammogram reading. Women of screening age are ready to accept the use of AI in breast screening but are less likely to use other AI-based health applications. A large number of women are undecided, or had mixed views, about the use of AI generally and they remain to be convinced that it can be trusted. 
• See BCS journal club presented by the authors here.

COVID-19 data repository and public attitudes retrospective

COVID-19 repository and public attitudes retrospective
Centre for Data Ethics and Innovation 5 March 2021

• New research on the use of AI and data-driven technology in the UK’s COVID-19 response, highlighting insights into public attitudes, as well as trends it has identified.

Ethics-Based Auditing to Develop Trustworthy AI

Ethics-Based Auditing to Develop Trustworthy AI.
Minds & Machines (2021). https://doi.org/10.1007/s11023-021-09557-8. 19 February 2021

• This article considers auditing as a promising mechanism to bridge the gap between principles and practice in AI ethics.

Abstract

The AI Ethics Initiative

The AI Ethics Initiative
NHS X February 2021

• The aim of the AI Ethics Initiative (part of the NHS X AI Lab) is to ensure that AI products used in the NHS and care settings will not exacerbate health inequalities.

What we still need to use AI safely and quickly in healthcare

What we still need to use AI safely and quickly in healthcare
Digital Health 19 January 2021

• Rachel Dunscombe, CEO of the NHS digital academy and director for Tektology, and Jane Rendall, UK managing director for Sectra, examine what needs to happen to make sure AI is used safely in healthcare.

AI Roadmap

AI Roadmap
AI Council 6 January 2021

• An independent report, carried out by the AI Council, providing recommendations to help the government's strategic direction on AI. The roadmap includes 16 recommendations to government around Research development & innovation, Skills and Diversity, Data, Infrastructure and Public Trust and National, Cross-sector Adoption.

Creating an international approach to AI for healthcare

Creating an international approach to AI for healthcare
NHS AI Lab, NHSX 1 December 2020

• High level policy recommendations on how best to support and facilitate the use of AI-driven technologies within health systems. [Commissioned by the Global Digital Health Partnership (GDHP)]

Explainability for artificial intelligence in healthcare: a multidisciplinary perspective.

Explainability for artificial intelligence in healthcare: a multidisciplinary perspective.
BMC Med Inform Decis Mak. 2020 Nov 30;20(1):310. doi: 10.1186/s12911-020-01332-6.

• This paper provides a comprehensive assessment of the role of explainability in medical AI and makes an ethical evaluation of what explainability means for the adoption of AI-driven tools into clinical practice.

CDEI Review into bias in algorithmic decision-making

Review into bias in algorithmic decision-making
Centre for Data Ethics and Innovation 27 November 2020

• A review the risks of bias in algorithmic decision-making. It presents formal recommendations to the government and advice for regulators and industry, aiming to support responsible innovation and help build a strong, trustworthy system of governance.
• Press release: CDEI proposes a roadmap to tackle algorithmic bias
• Comment: Ada Lovelace Institute 

Championing behind the scenes tech in healthcare

Championing behind the scenes tech in healthcare [Editorial]
Health Tech Digital 9 November 2020

• Editorial by John Gikopoulos, Global Head for Artificial Intelligence & Automation, Infosys Consulting on the use of AI in healthcare now and going forward.

Using predictive analytics in local public services

Using predictive analytics in local public services
LGA 5 November 2020

• This report aims to provide a guide for councils about predictive analytics and how they might use this technology.

The data will see you now: Datafication and the boundaries of health

The data will see you now: Datafication and the boundaries of health
Ada Lovelace Institute October 2020

• This report explores the datafication of health: what it is, how it occurs, and its impacts on individual and social wellbeing. It draws on examples to synthesise existing research, analyse concepts and surface the societal and ethical challenges arising from the blurring of the boundaries of health data.